ML Mastery: Deep Dive into Regression, SVM, Random Forests, and Clustering for Advanced Data Science Certification.
Sub Category
- Other IT & Software
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Objectives
- Understand the mathematical foundations behind supervised and unsupervised learning algorithms.
- Implement Linear and Logistic Regression from scratch using Python and NumPy for foundational understanding.
- Master Support Vector Machines (SVM) for complex classification tasks and high-dimensional data handling.
- Build robust ensemble models using Random Forests, AdaBoost, and Gradient Boosting techniques.
- Explore non-linear dimensionality reduction using t-SNE and PCA for data visualization and feature selection.
Pre Requisites
- Basic understanding of Python programming including loops and functions.
- Familiarity with high school level mathematics particularly Algebra and basic Calculus.
- Fundamental knowledge of statistics and probability concepts.
- A computer with Python 3 and a browser to access Jupyter Notebooks.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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